The fact that majority of commercial and government projects cost more and take longer than was initially planned is well known in the field of project cost and schedule management. The respective increase factor is ranging between about 30% and 100% depending on the nature of the projects. Self-consistent prediction of schedule probability and project cost remains unresolved problem of project management.
Commercial software packages conducting Monte Carlo simulations of project cost and schedules (“@Risk” from Palisade Corp., Oracle's “Crystal Ball” and others) require, as an input to the program, asymmetric probability distribution functions for project tasks or work breakdown structure (WBS) elements. Systematic positive difference is compulsory between the mean and most likely value of each task distribution function. These differences, unlike symmetric deviations that are strongly averaged for large groups of tasks, are summed as means contributing to the resulting distribution function and shifting it to longer times (or higher costs). Existing approach utilizes these asymmetric probability distribution functions as additional informational input needed before the statistical analysis starts. To provide statistically meaningful results, this approach requires multiple (thousands) simulations of the project schedule.
Additional information regarding task distribution functions needed for the statistical analysis and based on “expert's opinion” may become a serious drawback. The existing approach is suitable primarily for the later project stages when the project plan is stable, and tasks and their tolerances are well-defined. At the early stages, when the tasks are less certain, but major project decisions have to be made, an approach showing correct data tendencies for project cost and schedule, with very general assumptions on task durations or cost distribution functions, is in a great demand.